General statistical overviews of FILTER data

Temporal overview

p_year %>% 
  inner_join(poems,by=c("p_id")) %>%
  count(collection,year) %>%
  mutate(measure="yearly count") %>%
  union_all(
    p_year %>% # 10 year rolling mean
    distinct(year) %>% 
    left_join(p_year %>% distinct(year),sql_on="RHS.year BETWEEN LHS.year-5 AND LHS.year+5") %>%
    inner_join(p_year,by=c("year.y"="year")) %>%
    inner_join(poems,by=c("p_id")) %>%
    group_by(collection=collection,year=year.x) %>%
    summarize(n=n()/10,.groups="drop") %>%
    mutate(measure="10 year rolling mean")
  ) %>%
  filter(year>0,year<9999,collection!="literary") %>%
  ggplot(aes(x=year,y=n,color=measure)) +
  geom_point(data=~.x %>% filter(measure=="yearly count")) +
  geom_line(data=~.x %>% filter(measure=="10 year rolling mean")) +
  theme_hsci_discrete(base_family="Arial") + 
  theme(legend.justification=c(0,1), legend.position=c(0.02, 0.98), legend.background = element_blank(), legend.key=element_blank()) + 
  labs(color=NULL) +
  scale_y_continuous(breaks=seq(0,20000,by=2000),labels=scales::comma_format()) +
  ylab("Poems") +
  scale_x_continuous(breaks=seq(1000,2000,by=50)) +
  xlab("Year") +
  facet_wrap(~collection, ncol=1) +
  ggtitle("Number of poems by year and collection")

p_year %>% 
  filter(year %in% c(0,9999)) %>% 
  left_join(poems) %>% 
  count(collection,year) %>%
  ungroup() %>%
  gt() %>%
  tab_header(title=\Abnormal years\) %>%
  fmt_integer(n)

Overview of collectors

poems %>% 
  distinct(collection) %>%
  pull() %>%
  map(~p_col %>% 
    inner_join(poems %>% filter(collection==.x),by=c("p_id")) %>%
    count(col_id) %>%
    left_join(collectors,by=c("col_id")) %>%
    select(col_id,name,n) %>%
    collect() %>%
    mutate(col_id=fct_reorder(str_c(col_id,"|",name),n)) %>%
    mutate(col_id=fct_lump_n(col_id,n=100,w=n)) %>%
    mutate(col_id=fct_relevel(col_id,"Other")) %>%
    group_by(col_id) %>%
    tally(wt=n) %>% {
      ggplot(.,aes(x=col_id,y=n)) +
      geom_col() +
      geom_text(aes(label=p(n)),hjust='left',nudge_y = 100) +
      theme_hsci_discrete(base_family="Arial") +
      coord_flip() +
      labs(title=str_c("Collectors in ",.x))
    }
  )

Warning: 1 unknown level in `f`: Other

[[1]]

[[2]]

[[3]]

[[4]]

p_col %>% 
  anti_join(collectors) %>%
  count(col_id) %>%
  gt() %>%
  tab_header(title=\Collectors without a name\) %>%
  fmt_integer(n)

Geographical overview

d <- p_loc %>% 
  count(loc_id) %>% 
  inner_join(locations) %>%
  select(name,n) %>%
  collect()

poems_without_location <- poems %>% 
  anti_join(p_loc) %>% 
  count() %>% 
  pull()

unprojected_locations <- d %>%
  anti_join(polygons) %>%
  add_row(name=NA,n=poems_without_location)

polygons %>%
  left_join(d) %>%
  tm_shape() +
  tm_polygons(col='n', id='name', style='fisher', palette='plasma') +
  tm_layout(title=str_c(\Geographical overview. Missing \,unprojected_locations %>% tally(wt=n) %>% pull() %>% p,\ poems.\))

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Geographical overview. Missing 37,345 poems.
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Poem locations not mapped

unprojected_locations %>%
  arrange(desc(n)) %>%
  gt() %>%
  tab_header(\Poem locations not mapped\) %>%
  fmt_integer(n)

Geographical overview by collection

d <- p_loc %>% 
  left_join(poems) %>%
  count(collection,loc_id) %>% 
  ungroup() %>%
  inner_join(locations) %>%
  select(collection,name,n) %>%
  collect()

poems_without_location <- poems %>% 
  anti_join(p_loc) %>% 
  count(collection) %>% 
  collect() %>%
  mutate(name=NA_character_)

unprojected_locations <- d %>%
  anti_join(polygons) %>%
  union_all(poems_without_location)

poems %>% 
  distinct(collection) %>%
  pull() %>%
  map(~
    tm_shape(
      polygons %>%
        left_join(
          p_loc %>% 
            inner_join(poems %>% filter(collection==.x),by=c(\p_id\)) %>%
            count(loc_id) %>% 
            inner_join(locations) %>%
            select(name,n) %>%
            collect()
        )
    ) +
    tm_polygons(col='n', id='name', style='fisher', palette='plasma') +
    tm_layout(title=str_c(\Geography of \,.x,\. Missing \,unprojected_locations %>% filter(collection==.x) %>% tally(wt=n) %>% pull() %>% p,\ poems.\))
  )

[[1]]

[[2]]

[[3]]

[[4]]

Poem locations not mapped by collection

poems %>% 
  distinct(collection) %>%
  pull() %>%
  map(~
    unprojected_locations %>%
      filter(collection==.x) %>%
      arrange(desc(n)) %>%
      select(-collection) %>%
      gt() %>%
      tab_header(str_c("Poem locations not mapped in ",.x)) %>%
      fmt_integer(n)
  )

[[1]]

Poem locations not mapped in skvr
name	n
Narvusi	3,202
Vuole	1,524
Uusikirkko Vpl	1,131
Pyhäjärvi Vpl	785
Etelä-Karjala	658
Pohjois-Pohjanmaa	557
Länsipohja	396
Kiimaisjärvi	363
Etelä-Savo	350
Tveri	294
Viipuri mlk	277
Salo	198
Pieksämäki	183
Pyhäjärvi Ol	160
Koski Hl	134
Pohjois-Karjala	129
Uusikirkko Tl	116
Tulomajärvi	115
Tornio	113
Porvoo mlk	112
Pyhäjärvi Ul	102
Pohjois-Savo	98
Viena	98
Satakunta	83
Säräisniemi	74
Uusikaupunki	71
Heinola mlk	62
Häme	61
Ruija	60
Jyväskylä mlk	52
Keski-Inkeri	49
Hämeenlinna	48
Kovero	47
Tuutari=Tuuteri	45
Laatokan Karjala (Raja-Karjala)	44
Itä- ja Pohjois-Inkeri	42
Kainuu	38
Kuopio mlk	35
Peräpohjola	33
Varkaus	30
Kajaani mlk	30
Etelä-Pohjanmaa	27
Keminmaa	26
Varsinais-Suomi	24
Mänttä	22
Novgorodin alue	22
Kotka	20
Mikkeli mlk	17
Karjala Tl	16
Alajärvi	16
Kimito	15
Prunkkala	14
Savonlinna	14
Sortavala mlk	13
Revonlahti-Revolax	12
Alakiiminki	11
Taipale (Enontaipale)	9
Helsingin pit	7
Sulva-Solf-Solv	7
Siuntio	6
Mustasaari-Korsholm	6
Siipyy-Sideby	6
Uzmana	6
Suma	6
Lapväärtti-Lappfjärd	5
Vaasa	5
Lappeenranta	4
Kenjärvi	4
Vammala	3
Kirkkonummi	3
Kokkola-Gamlakarleby	3
Kuolajärvi	3
Uusimaa	2
Iisalmi mlk	2
Pietarsaari-Jakobstad	2
Jepua-Jeppo	2
Kouta	2
Länsi-Inkeri	2
Uusikaupunki mlk	1
Anjalankoski	1
Keski-Suomi	1
Lieksa	1
Raippaluoto-Replot	1
Kuhmoniemi	1

[[2]]

Poem locations not mapped in erab
name	n
NA	2,008
välismaa	1,822
Tartu	973
Tallinn	685
Viljandi l.	602
Pärnu l.	598
Viljandimaa	480
Võrumaa	452
Narva l.	422
Läänemaa	329
Rakvere l.	304
Saaremaa	205
Valga	150
Pärnumaa	102
Paide l.	88
Haapsalu	86
Võru l.	60
Valgamaa	58
Sõrve	40
Järvamaa	39
Virumaa	36
Tartumaa	29
Hiiumaa	23
Harjumaa	17
Kuressaare l.	3

[[3]]

Poem locations not mapped in jr
name	n
Narvusi	1,780
NA	1,591
Peräpohjola	809
Uusikirkko Vpl	803
Viena	773
Itä- ja Pohjois-Inkeri	757
Hämeenlinna	517
Keski-Inkeri	483
Viron Inkeri	433
Länsi-Inkeri	424
Sortavala mlk	406
Uusikaupunki	386
Tulomajärvi	353
Keminmaa	349
Pohjois-Pohjanmaa	252
Savonlinna	240
Pyhäjärvi Vpl	215
Lahti	189
Pyhäjärvi Ol	164
Pieksämäki	163
Säräisniemi	159
Vuole	155
Etelä-Pohjanmaa	154
Mikkeli mlk	144
Pyhäjärvi Ul	143
Viipuri mlk	123
Kuusankoski	115
Etelä-Karjala	112
Pohjois-Karjala	111
Pohjois-Savo	110
Tveri	110
Salo	107
Riihimäki	104
Helsingin pit	96
Mänttä	91
Kiimaisjärvi	85
Lapväärtti-Lappfjärd	82
Kainuu	81
Kotka	76
Alajärvi	75
Toijala	70
Laatokan Karjala (Raja-Karjala)	63
Etelä-Savo	62
Uusikirkko Tl	61
Lappeenranta	59
Tornio	59
Heinola mlk	55
Tuutari=Tuuteri	55
Pohjois-Pirkkala	47
Uusimaa	44
Kuopio mlk	42
Karjala Tl	35
Varkaus	32
Ruija	31
Kajaani mlk	30
Porvoo mlk	22
Storfjord	20
Parainen	18
Lappi Tl	17
Jyväskylä mlk	17
Vaasa	17
Vaala	17
Karkkila	16
Kouvola	16
Rovaniemi mlk	16
Kerava	15
Valkeakoski	15
Lieksa	15
Kuusisto	13
Muoslompolo	13
Uusikaupunki mlk	12
Hongonjoki	12
Satakunta	12
Novgorodin alue	12
Kemiö	11
Salo	11
Pieksämäki mlk	11
Iisalmi mlk	11
Pielisensuu	11
Särkisalo	10
Hamina	10
Kokkola-Gamlakarleby	10
Kristiinankaupunki-Kristinestad	10
Revonlahti-Revolax	10
Nauvo	9
Junosuando	9
Karesuando	9
Jämsänkoski	8
Koski Hl	8
Äänislinna	8
Kuhmoniemi	7
Muurmanni	7
Häme	6
Suolahti	6
Taipale	6
Mustasaari-Korsholm	6
Länsipohja	6
Rauma mlk	5
Kuolajärvi	5
Kistrand	5
Moseija	5
Kenjärvi	5
Imatra	4
Uusikaarlepyy-Nykarleby	4
Siipyy-Sideby	4
Yliveteli	4
Lauritsala	3
Ruukki	3
Koutokeino	3
Riipuskala	3
Varsinais-Suomi	2
Vammala	2
Järvenpää	2
Tiudia	2
Jaama	2
Prunkkala	1
Ikaalinen mlk	1
Myllykoski	1
Kirkkonummi	1
Loviisa	1
Sipoo	1
Nurmes mlk	1
Petolahti-Petalax	1
Oulu mlk	1
Nordkapp	1
Maasöy	1
Muodoslompolo	1
Pietari=Leningrad	1
Siestarjoki	1
Ahvenanmaa	1

[[4]]

Poem locations not mapped in literary
name	n
NA	727

NA

Spatiotemporal overview

d <- poems %>%
  left_join(p_year %>% mutate(year=if_else(year %in% c(0L,9999L),NA,year))) %>% 
  collect() %>%
  mutate(year_ntile=ntile(year,11)) %>%
  group_by(year_ntile) %>%
  mutate(years=str_c(min(year),\-\,max(year))) %>%
  ungroup() %>%
  left_join(p_loc %>% collect()) %>% 
  count(years,loc_id) %>% 
  ungroup() %>%
  left_join(locations %>% select(loc_id,name) %>% collect())

polygons %>% 
  left_join(d %>% complete(name,years)) %>%
  tm_shape() +
  tm_polygons(col='n', id='name', style='fisher', palette='plasma') +
  tm_layout(main.title="Geographical overviews by time",legend.outside.size=0.1) +
  tm_facets(by="years",ncol=4)

Joining, by = "name"

Poem verse statistics

Line types

d <- verses %>% 
  left_join(verse_poem) %>% 
  left_join(poems) %>% 
  count(collection,type) %>% 
  arrange(collection,desc(n)) %>%
  collect()

Joining, by = "v_id"Joining, by = "p_id"

d %>% 
  group_by(collection) %>%
  mutate(proportion=n/sum(n)) %>%
  gt() %>%
  fmt_integer(n) %>%
  fmt_percent(proportion)

type	n	proportion
skvr
V	1,340,987	94.63%
L	44,303	3.13%
CPT	27,869	1.97%
K	3,931	0.28%
erab
V	1,861,583	93.39%
PAG	53,040	2.66%
CPT	19,844	1.00%
L	18,465	0.93%
TYH	18,357	0.92%
REF	17,869	0.90%
LRY	3,868	0.19%
RRE	307	0.02%
MRK	52	0.00%
U	38	0.00%
LLI	2	0.00%
TYP	1	0.00%
jr
V	812,343	90.94%
L	49,411	5.53%
CPT	28,030	3.14%
K	3,502	0.39%
literary
V	82,460	97.54%
L	1,220	1.44%
CPT	777	0.92%
K	87	0.10%

Verse line lengths

d_nr_characters <- verses_cl %>%
  mutate(nr_characters=str_length(text)) %>%
  left_join(verse_poem) %>% 
  left_join(poems) %>% 
  count(collection,nr_characters) %>% 
  ungroup() %>%
  arrange(collection,desc(n)) %>%
  collect()

Joining, by = "v_id"Joining, by = "p_id"

Verse line lengths in characters

d_nr_characters %>% 
  filter(nr_characters<=60) %>%
  ggplot(aes(x=nr_characters,y=n)) +
  geom_col(width=1) +
  facet_wrap(~collection,scales="free_y") +
  theme_hsci_discrete(base_family="Arial") +
  scale_y_continuous(labels=scales::comma_format()) +
  labs(title="Number of characters in verse lines")

d_nr_characters %>% 
  group_by(collection) %>%
  mutate(prop=n/sum(n)) %>%
  ungroup() %>%
  filter(nr_characters<=60) %>%
  ggplot(aes(x=nr_characters,y=collection,fill=collection,height=prop)) +
  geom_density_ridges(stat='identity') +
  theme_hsci_discrete(base_family="Arial") +
#  scale_y_continuous(labels=scales::percent_format()) +
  labs(title="Number of characters in verse lines")

Verse lines with more than 60 characters

d_nr_characters %>% 
  mutate(nl=if_else(nr_characters>60,n,0L)) %>%
  group_by(collection) %>%
  summarise(lines=sum(nl),proportion=sum(nl)/sum(n),.groups="drop") %>%
  arrange(desc(lines)) %>%
  gt() %>%
  tab_header(title="Verse lines with more than 60 characters") %>%
  fmt_integer(lines) %>%
  fmt_percent(proportion)

Verse lines with more than 60 characters
collection	lines	proportion
jr	1,911	0.24%
erab	291	0.02%
skvr	202	0.02%
literary	1	0.00%

Verse line lengths in words

d_nr_words %>% 
  filter(nr_words<=10) %>%
  ggplot(aes(x=nr_words,y=n)) +
  geom_col(width=1) +
  facet_wrap(~collection,scales="free_y") +
  scale_x_continuous(breaks=seq(0,10,by=2)) +
  scale_y_continuous(labels=scales::comma_format()) +
  theme_hsci_discrete(base_family="Arial") +
  labs(title="Number of words in verse lines")

d_nr_words %>% 
  filter(nr_words<=10) %>%
  uncount(n) %>%
  ggplot(aes(x=nr_words,y=collection,fill=collection)) +
  stat_binline(binwidth=1) +
  theme_hsci_discrete(base_family="Arial") +
  scale_x_continuous(breaks=seq(0,10,by=2)) +
#  scale_y_continuous(labels=scales::percent_format()) +
  labs(title="Number of words in verse lines")

Verse lines with more than 10 words

d_nr_words %>% 
  mutate(nl=if_else(nr_words>10,n,0L)) %>%
  group_by(collection) %>%
  summarise(lines=sum(nl),proportion=sum(nl)/sum(n),.groups="drop") %>%
  arrange(desc(lines)) %>%
  gt() %>%
  tab_header(title="Verse lines with more than 10 words") %>%
  fmt_integer(lines) %>%
  fmt_percent(proportion)

Verse lines with more than 10 words
collection	lines	proportion
jr	839	0.11%
erab	257	0.01%
skvr	38	0.00%
literary	9	0.01%

d <- word_occ %>%
  inner_join(words %>%
    mutate(nr_characters=str_length(text))) %>%
  group_by(v_id) %>%
  summarise(nr_characters,nr_words=max(pos)) %>%
  left_join(verse_poem) %>% 
  left_join(poems) %>% 
  count(collection,nr_words,pos,nr_characters) %>%
  collect()

Joining, by = "w_id"Joining, by = "v_id"Joining, by = "p_id"Error:  [0]

d %>%
  group_by(collection,nr_words,pos) %>%
  mutate(prop=n/sum(n)) %>%
  ungroup() %>%
  filter(nr_words>=2,nr_words<6) %>%
  mutate(nr_words=as_factor(nr_words),pos=as_factor(pos)) %>%
  uncount(n) %>%
  ggplot(aes(x=nr_characters,y=nr_words,fill=nr_words)) +
  stat_binline(binwidth=1) +
  facet_grid(collection~pos,labeller = labeller(pos=label_both)) + 
  xlab("Number of characters in word") +
  ylab("Number of words in verse") +
  labs(
    title="Number of characters in words by their position",
    subtitle="According to length of verse and collection"
    ) +
  theme_hsci_discrete(base_family="Arial")